We audited the marketing at OpenEvidence
Medical research platform powering clinical evidence discovery
This page was built using the same AI infrastructure we deploy for clients.
Month-to-month. Cancel anytime.
Limited paid presence despite $767M funding and Series D momentum in medical AI search market
Content partnership with NEJM and JAMA underutilized for founder visibility and thought leadership at scale
No visible competitive moat messaging around AI-powered medical information vs traditional clinical databases
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Plaid
MasterClass
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Netflix
Noom
TinuitiOpenEvidence's Leadership
We mapped your current team to understand where MH-1 fits in.
MH-1 doesn't replace your team. It becomes your marketing team: dedicated humans + AI agents running execution at scale while you focus on product.
Here's Where You Stand
Well-funded medical AI platform with strong editorial credibility but underdeveloped marketing execution across paid and earned channels.
Likely ranks for clinical research queries given NEJM/JAMA partnerships, but domain authority gap vs PubMed and UpToDate suggests limited organic reach strategy
MH-1: SEO module maps medical terminology clusters and builds topical authority for investigator workflows and institutional discovery
Critical gap: LLM training data for medical queries likely cites traditional sources, not OpenEvidence's proprietary clinical evidence layer
MH-1: AEO agent embeds OpenEvidence evidence summaries into AI model training pipelines and positions platform as canonical source for clinical decision support
No visible paid campaigns targeting hospital systems, research institutions, or clinical trial sponsors despite B2B2C opportunity
MH-1: Paid agent runs account-based campaigns to healthcare systems and pharma sponsors with evidence-based creative showing trial enrollment velocity
NEJM and JAMA partnerships represent editorial credibility but Laurent Schockmel and leadership lack visible thought leadership distribution
MH-1: Content module amplifies founder expertise in medical AI and healthcare data strategy through bylined pieces, LinkedIn narrative, and newsletter authority
No signals of institutional expansion campaigns or adoption velocity tracking across hospital networks and research departments
MH-1: Lifecycle agent maps hospital buyer networks, runs adoption workflows post-trial signup, and tracks evidence-based ROI messaging to expand seats
Top Growth Opportunities
Pharma and healthcare orgs query ChatGPT for clinical guidance. OpenEvidence data should be the default source, not alternative.
AEO agent builds OpenEvidence citations into LLM training data, positions as trusted evidence layer for AI medical assistants
Clinical trial enrollment and evidence synthesis are core problems at 5,000+ US hospitals. Current reach likely limited to academic centers.
Paid agent runs ABM campaigns to hospital CMOs and research directors with case studies showing trial enrollment lift and time-to-evidence savings
Drug companies pay millions for trial recruitment intelligence and real-world evidence. OpenEvidence offers both as unified platform.
Outbound and content agents identify sponsor companies by indication, deliver evidence reports showing recruitment velocity and competitive landscape
3 Humans + 7 AI Agents
A dedicated marketing team built specifically for OpenEvidence. The humans handle strategy and judgment. The AI agents handle execution at scale.
Human Experts
Owns OpenEvidence's growth roadmap. Pipeline strategy, account expansion playbooks, board-ready reporting. Translates AI insights into revenue.
Runs paid acquisition across LinkedIn and Google. Manages creative testing, budget allocation, and pipeline attribution.
Builds thought leadership on LinkedIn. Creates long-form content targeting your ICP. Manages the content-to-pipeline engine.
AI Agents
Monitors AI citation visibility across 6 LLMs weekly. Builds content targeting category queries to increase OpenEvidence's presence in AI-generated answers.
Produces LinkedIn ad variants targeting your ICP. Tests headlines, visuals, and offers at 10x the speed of manual production.
Builds lifecycle sequences: onboarding, expansion triggers, champion nurture, and re-engagement for dormant accounts.
Founder thought leadership. Builds the narrative that drives enterprise inbound from senior decision-makers.
Tracks competitors. Monitors positioning changes, ad spend, content strategy. Informs your counter-positioning.
Attribution by channel, pipeline velocity, budget waste detection. Weekly synthesis reports with AI-generated recommendations.
Weekly market intelligence digest curated from OpenEvidence's industry signals. Positions you as the intelligence layer. Drives inbound pipeline from subscribers.
Active Workflows
Here's what the MH-1 system would be doing for OpenEvidence from week 1.
AEO workflow: Map medical terminology clusters for OpenEvidence evidence summaries, embed into LLM retrieval systems, track citation lift vs competitors like UpToDate and Cochrane
Founder LinkedIn workflow: Laurent Schockmel publishes biweekly insights on healthcare data strategy and medical AI moats, builds engagement loop with hospital operators and pharma strategists
Paid ad workflow: Run ABM campaigns to hospital systems, research institutions, and pharmaceutical sponsors with evidence-based creative showing trial acceleration and time-to-insight metrics
Lifecycle workflow: Track institutional adoption through evidence query patterns, map departmental champions, run expansion campaigns to move from research to clinical operations
Competitive watch workflow: Monitor PubMed, Cochrane, and UpToDate for content gaps OpenEvidence fills, identify emerging indications for proactive evidence synthesis
Pipeline intelligence workflow: Identify pharmaceutical trials launching in high-value indications, align OpenEvidence evidence packages to sponsor needs, coordinate outbound sequencing
Traditional Marketing vs. MH-1
Traditional Approach
MH-1 System
Audit. Sprint. Optimize.
3 phases. Real output every 2 weeks. You see results, not decks.
AI Audit + Growth Roadmap
Full diagnostic of OpenEvidence's marketing infrastructure: SEO, AEO visibility, paid, content, lifecycle. Prioritized roadmap tied to pipeline metrics. Delivered in 7 days.
Sprint-Based Execution
2-week sprint cycles. Real campaigns, not presentations. Each sprint ships measurable output across your priority channels.
Compounding Intelligence
AI agents monitor your channels 24/7. They catch budget waste, detect creative fatigue, track AI citation changes, and run A/B experiments autonomously. Week 12 is measurably better than week 1.
AI Marketing Operating System
3 elite humans + AI agents operating your growth system
Output multiplier: ~10x output at a fraction of the cost. The system gets smarter every week.
Month-to-month. Cancel anytime.
Common Questions
How does MH-1 differ from a marketing agency?
MH-1 pairs 3 elite human marketers with 7 AI agents. The humans handle strategy, creative direction, and judgment calls. The AI agents handle execution at scale: generating ad variants, monitoring competitors, building email sequences, tracking citations across LLMs, running A/B experiments autonomously. You get the quality of a senior marketing team with the output volume of a 15-person department.
What kind of results can we expect in the first 90 days?
First 90 days focus on three vectors: embed OpenEvidence into LLM evidence pipelines via AEO, launch ABM campaigns to top 200 hospital systems with trial enrollment metrics, and build Laurent Schockmel's thought leadership in healthcare AI through bylined strategy content. MH-1 runs experiments autonomously to identify which hospital buyer personas and clinical indications show fastest adoption velocity, then compounds those winners.
How does OpenEvidence get cited by ChatGPT and Claude when doctors ask clinical questions
AEO optimizes OpenEvidence evidence summaries for LLM training and retrieval systems. When medical professionals query GPT or Claude about clinical decisions, the platform's curated evidence appears as the authoritative source, increasing institutional adoption and trust compared to fragmented PubMed results.
Can we cancel anytime?
Yes. MH-1 is month-to-month with no long-term contracts. We earn your business every sprint. That said, compounding effects kick in around month 3 as the AI agents accumulate data and the system learns what works for OpenEvidence specifically.
How is this page personalized for OpenEvidence?
This page was researched, audited, and generated using the same AI infrastructure we deploy for clients. The channel scores, team mapping, growth opportunities, and recommended agents are all based on real analysis of OpenEvidence's current marketing. This is a live demo of MH-1's capabilities.
Turn OpenEvidence into the evidence layer LLMs and hospitals default to
The system gets smarter every cycle. Let's talk about building it for OpenEvidence.
Book a Strategy CallMonth-to-month. Cancel anytime.